advanced-games-library/src/services/ai/AdvancedAIEngine.ts

Advanced Gaming Library for React Native - Four Complete Games with iOS Compatibility Fixes

import * as React from 'react';

/**
 * Advanced AI System for Games
 * Machine learning-powered opponents, adaptive difficulty, and intelligent game assistance
 */

export interface AIPlayerConfig {
  id: string;
  name: string;
  difficulty: 'easy' | 'medium' | 'hard' | 'expert' | 'adaptive';
  personality: AIPersonality;
  learningEnabled: boolean;
  adaptationRate: number; // 0-1, how quickly AI adapts to player
  specializations: string[]; // Types of games this AI excels at
}

export interface AIPersonality {
  aggression: number; // 0-1, how aggressive the AI plays
  risk_tolerance: number; // 0-1, willingness to take risks
  pattern_recognition: number; // 0-1, ability to recognize player patterns
  creativity: number; // 0-1, tendency to try unconventional moves
  consistency: number; // 0-1, how consistent the AI's performance is
  adaptation_speed: number; // 0-1, how quickly AI adapts strategies
}

export interface GamePattern {
  type: 'move_sequence' | 'timing_pattern' | 'strategy_preference' | 'weakness';
  pattern: any[];
  confidence: number;
  frequency: number;
  effectiveness: number;
  lastSeen: Date;
}

export interface AIDecision {
  action: any;
  confidence: number;
  reasoning: string[];
  alternatives: Array<{ action: any; score: number; reasoning: string }>;
  executionTime: number;
  patternsBased: string[];
}

export interface PlayerProfile {
  playerId: string;
  skillLevel: number; // 0-100
  preferredStrategies: string[];
  identifiedWeaknesses: string[];
  gamePatterns: Map<string, GamePattern>;
  playStyle: {
    speed: number; // Average move time
    accuracy: number; // Success rate
    consistency: number; // Performance variance
    adaptability: number; // How quickly player adapts to AI
  };
  emotionalState: {
    frustration: number; // 0-1
    engagement: number; // 0-1
    confidence: number; // 0-1
  };
}

/**
 * Advanced AI Engine with machine learning capabilities
 */
export class AdvancedAIEngine {
  private static instance: AdvancedAIEngine;
  private aiPlayers = new Map<string, AIPlayerConfig>();
  private playerProfiles = new Map<string, PlayerProfile>();
  private gameHistory = new Map<string, GameHistoryEntry[]>();
  private neuralNetwork?: SimpleNeuralNetwork;
  private patternRecognizer: PatternRecognizer;
  private difficultyAdapter: DifficultyAdapter;
  private isLearningEnabled = true;

  private constructor() {
    this.neuralNetwork = new SimpleNeuralNetwork();
    this.patternRecognizer = new PatternRecognizer();
    this.difficultyAdapter = new DifficultyAdapter();
    this.initializeDefaultAI();
  }

  static getInstance(): AdvancedAIEngine {
    if (!AdvancedAIEngine.instance) {
      AdvancedAIEngine.instance = new AdvancedAIEngine();
    }
    return AdvancedAIEngine.instance;
  }

  /**
   * Create AI player with specified configuration
   */
  createAIPlayer(config: Partial<AIPlayerConfig>): string {
    const aiId = `ai_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
    
    const fullConfig: AIPlayerConfig = {
      id: aiId,
      name: config.name || `AI Player ${aiId.slice(-4)}`,
      difficulty: config.difficulty || 'medium',
      personality: config.personality || this.getDefaultPersonality(),
      learningEnabled: config.learningEnabled ?? true,
      adaptationRate: config.adaptationRate ?? 0.3,
      specializations: config.specializations || []
    };

    this.aiPlayers.set(aiId, fullConfig);
    return aiId;
  }

  /**
   * Get AI decision for game move
   */
  async getAIDecision(
    aiId: string, 
    gameState: any, 
    availableActions: any[], 
    opponentId?: string
  ): Promise<AIDecision> {
    const aiPlayer = this.aiPlayers.get(aiId);
    if (!aiPlayer) {
      throw new Error(`AI player ${aiId} not found`);
    }

    const startTime = performance.now();
    
    // Get opponent profile for adaptive behavior
    const opponentProfile = opponentId ? this.playerProfiles.get(opponentId) : null;
    
    // Analyze current game state
    const stateAnalysis = this.analyzeGameState(gameState, aiPlayer, opponentProfile);
    
    // Generate possible moves with scores
    const actionScores = await this.evaluateActions(
      availableActions, 
      gameState, 
      aiPlayer, 
      opponentProfile
    );
    
    // Apply personality and difficulty modifiers
    const modifiedScores = this.applyPersonalityModifiers(actionScores, aiPlayer);
    
    // Select best action based on difficulty and personality
    const selectedAction = this.selectAction(modifiedScores, aiPlayer);
    
    const executionTime = performance.now() - startTime;
    
    // Create decision object
    const decision: AIDecision = {
      action: selectedAction.action,
      confidence: selectedAction.score,
      reasoning: selectedAction.reasoning.split(', '),
      alternatives: modifiedScores.slice(0, 3).map(score => ({
        action: score.action,
        score: score.score,
        reasoning: score.reasoning
      })),
      executionTime,
      patternsBased: stateAnalysis.patternsUsed
    };
    
    // Learn from this decision if learning is enabled
    if (aiPlayer.learningEnabled && this.isLearningEnabled) {
      this.recordDecision(aiId, gameState, decision, opponentId);
    }
    
    return decision;
  }

  /**
   * Update player profile based on game actions
   */
  updatePlayerProfile(playerId: string, gameData: {
    action: any;
    gameState: any;
    result: 'success' | 'failure' | 'neutral';
    timeSpent: number;
  }): void {
    let profile = this.playerProfiles.get(playerId);
    
    if (!profile) {
      profile = this.createPlayerProfile(playerId);
      this.playerProfiles.set(playerId, profile);
    }
    
    // Update play style metrics
    this.updatePlayStyle(profile, gameData);
    
    // Detect and update patterns
    this.patternRecognizer.updatePatterns(profile, gameData);
    
    // Update emotional state based on performance
    this.updateEmotionalState(profile, gameData.result, gameData.timeSpent);
    
    // Adapt AI difficulty if needed
    this.difficultyAdapter.updateDifficulty(profile, gameData);
  }

  /**
   * Get adaptive difficulty recommendation
   */
  getAdaptiveDifficulty(playerId: string, gameType: string): {
    difficulty: string;
    reasoning: string;
    adjustments: Record<string, number>;
  } {
    const profile = this.playerProfiles.get(playerId);
    if (!profile) {
      return {
        difficulty: 'medium',
        reasoning: 'No player data available, using default difficulty',
        adjustments: {}
      };
    }
    
    return this.difficultyAdapter.calculateDifficulty(profile, gameType);
  }

  /**
   * Analyze game state for patterns and opportunities
   */
  private analyzeGameState(gameState: any, aiPlayer: AIPlayerConfig, opponentProfile?: PlayerProfile | null): {
    opportunities: string[];
    threats: string[];
    patterns: GamePattern[];
    patternsUsed: string[];
  } {
    const analysis = {
      opportunities: [] as string[],
      threats: [] as string[],
      patterns: [] as GamePattern[],
      patternsUsed: [] as string[]
    };
    
    // Look for opponent patterns if available
    if (opponentProfile) {
      opponentProfile.gamePatterns.forEach((pattern, patternId) => {
        if (pattern.confidence > 0.7) {
          analysis.patterns.push(pattern);
          analysis.patternsUsed.push(patternId);
          
          // Convert patterns to opportunities or threats
          if (pattern.type === 'weakness') {
            analysis.opportunities.push(`Exploit opponent weakness: ${pattern.type}`);
          } else if (pattern.type === 'strategy_preference') {
            analysis.threats.push(`Counter opponent strategy: ${pattern.type}`);
          }
        }
      });
    }
    
    return analysis;
  }

  /**
   * Evaluate available actions and assign scores
   */
  private async evaluateActions(
    actions: any[], 
    gameState: any, 
    aiPlayer: AIPlayerConfig, 
    opponentProfile?: PlayerProfile | null
  ): Promise<Array<{ action: any; score: number; reasoning: string }>> {
    const evaluatedActions: Array<{ action: any; score: number; reasoning: string }> = [];
    
    for (const action of actions) {
      let score = 0;
      const reasoning: string[] = [];
      
      // Base score from neural network if available
      if (this.neuralNetwork) {
        const networkScore = await this.neuralNetwork.evaluate(gameState, action);
        score += networkScore * 0.4;
        reasoning.push(`Neural network score: ${networkScore.toFixed(2)}`);
      }
      
      // Strategic value based on game state
      const strategicScore = this.calculateStrategicValue(action, gameState);
      score += strategicScore * 0.3;
      reasoning.push(`Strategic value: ${strategicScore.toFixed(2)}`);
      
      // Counter-opponent patterns if available
      if (opponentProfile) {
        const counterScore = this.calculateCounterValue(action, opponentProfile);
        score += counterScore * 0.2;
        reasoning.push(`Counter-pattern value: ${counterScore.toFixed(2)}`);
      }
      
      // Random factor for unpredictability
      const randomFactor = (Math.random() - 0.5) * 0.1;
      score += randomFactor;
      reasoning.push(`Randomization: ${randomFactor.toFixed(3)}`);
      
      evaluatedActions.push({
        action,
        score: Math.max(0, Math.min(1, score)),
        reasoning: reasoning.join(', ')
      });
    }
    
    return evaluatedActions.sort((a, b) => b.score - a.score);
  }

  /**
   * Apply AI personality modifiers to action scores
   */
  private applyPersonalityModifiers(
    actionScores: Array<{ action: any; score: number; reasoning: string }>, 
    aiPlayer: AIPlayerConfig
  ): Array<{ action: any; score: number; reasoning: string }> {
    const { personality } = aiPlayer;
    
    return actionScores.map(actionScore => {
      let modifiedScore = actionScore.score;
      const modificationReasons: string[] = [];
      
      // Apply aggression modifier
      if (this.isAggressiveAction(actionScore.action)) {
        const aggressionBonus = (personality.aggression - 0.5) * 0.2;
        modifiedScore += aggressionBonus;
        modificationReasons.push(`Aggression: ${aggressionBonus.toFixed(2)}`);
      }
      
      // Apply risk tolerance
      if (this.isRiskyAction(actionScore.action)) {
        const riskBonus = (personality.risk_tolerance - 0.5) * 0.15;
        modifiedScore += riskBonus;
        modificationReasons.push(`Risk tolerance: ${riskBonus.toFixed(2)}`);
      }
      
      // Apply creativity factor
      if (this.isCreativeAction(actionScore.action)) {
        const creativityBonus = personality.creativity * 0.1;
        modifiedScore += creativityBonus;
        modificationReasons.push(`Creativity: ${creativityBonus.toFixed(2)}`);
      }
      
      // Add consistency variation
      const consistencyVariation = (1 - personality.consistency) * (Math.random() - 0.5) * 0.2;
      modifiedScore += consistencyVariation;
      modificationReasons.push(`Consistency variation: ${consistencyVariation.toFixed(3)}`);
      
      return {
        ...actionScore,
        score: Math.max(0, Math.min(1, modifiedScore)),
        reasoning: actionScore.reasoning + (modificationReasons.length > 0 ? ` | Personality: ${modificationReasons.join(', ')}` : '')
      };
    });
  }

  /**
   * Select action based on difficulty and personality
   */
  private selectAction(
    actionScores: Array<{ action: any; score: number; reasoning: string }>, 
    aiPlayer: AIPlayerConfig
  ): { action: any; score: number; reasoning: string } {
    if (actionScores.length === 0) {
      throw new Error('No actions available for AI to choose from');
    }
    
    // Difficulty-based selection strategy
    switch (aiPlayer.difficulty) {
      case 'easy':
        // 60% chance of suboptimal move
        return Math.random() < 0.6 ? 
          actionScores[Math.floor(Math.random() * Math.min(3, actionScores.length))] :
          actionScores[0];
      
      case 'medium':
        // 30% chance of suboptimal move
        return Math.random() < 0.3 ? 
          actionScores[Math.floor(Math.random() * Math.min(2, actionScores.length))] :
          actionScores[0];
      
      case 'hard':
        // 10% chance of suboptimal move
        return Math.random() < 0.1 ? 
          actionScores[1] || actionScores[0] :
          actionScores[0];
      
      case 'expert':
        // Always choose optimal move
        return actionScores[0];
      
      case 'adaptive':
        // Dynamic selection based on opponent performance
        const selectionIndex = this.calculateAdaptiveSelection(actionScores, aiPlayer);
        return actionScores[selectionIndex] || actionScores[0];
      
      default:
        return actionScores[0];
    }
  }

  // Helper methods for AI decision making
  private calculateStrategicValue(action: any, gameState: any): number {
    // Simplified strategic evaluation - would be game-specific
    return Math.random() * 0.8 + 0.1;
  }

  private calculateCounterValue(action: any, opponentProfile: PlayerProfile): number {
    let counterValue = 0;
    opponentProfile.gamePatterns.forEach(pattern => {
      if (pattern.confidence > 0.6) {
        counterValue += pattern.confidence * 0.3;
      }
    });
    return Math.min(1, counterValue);
  }

  private isAggressiveAction(action: any): boolean {
    return Math.random() < 0.3; // Placeholder
  }

  private isRiskyAction(action: any): boolean {
    return Math.random() < 0.2; // Placeholder
  }

  private isCreativeAction(action: any): boolean {
    return Math.random() < 0.15; // Placeholder
  }

  private calculateAdaptiveSelection(
    actionScores: Array<{ action: any; score: number; reasoning: string }>, 
    aiPlayer: AIPlayerConfig
  ): number {
    const randomFactor = Math.random();
    if (randomFactor < 0.7) return 0;
    if (randomFactor < 0.9) return 1;
    return 2;
  }

  // Player profile management
  private createPlayerProfile(playerId: string): PlayerProfile {
    return {
      playerId,
      skillLevel: 50,
      preferredStrategies: [],
      identifiedWeaknesses: [],
      gamePatterns: new Map(),
      playStyle: {
        speed: 0.5,
        accuracy: 0.5,
        consistency: 0.5,
        adaptability: 0.5
      },
      emotionalState: {
        frustration: 0,
        engagement: 0.7,
        confidence: 0.5
      }
    };
  }

  private updatePlayStyle(profile: PlayerProfile, gameData: any): void {
    const { timeSpent, result } = gameData;
    
    // Update speed
    const speedSample = Math.max(0, Math.min(1, 1 - (timeSpent / 10000)));
    profile.playStyle.speed = (profile.playStyle.speed * 0.9) + (speedSample * 0.1);
    
    // Update accuracy
    const accuracySample = result === 'success' ? 1 : (result === 'neutral' ? 0.5 : 0);
    profile.playStyle.accuracy = (profile.playStyle.accuracy * 0.9) + (accuracySample * 0.1);
  }

  private updateEmotionalState(profile: PlayerProfile, result: string, timeSpent: number): void {
    const { emotionalState } = profile;
    
    if (result === 'failure') {
      emotionalState.frustration = Math.min(1, emotionalState.frustration + 0.1);
      emotionalState.confidence = Math.max(0, emotionalState.confidence - 0.05);
    } else if (result === 'success') {
      emotionalState.frustration = Math.max(0, emotionalState.frustration - 0.05);
      emotionalState.confidence = Math.min(1, emotionalState.confidence + 0.1);
    }
    
    const engagementSample = timeSpent > 1000 ? 1 : 0.5;
    emotionalState.engagement = (emotionalState.engagement * 0.95) + (engagementSample * 0.05);
  }

  private recordDecision(aiId: string, gameState: any, decision: AIDecision, opponentId?: string): void {
    const historyEntry: GameHistoryEntry = {
      timestamp: new Date(),
      aiId,
      gameState,
      decision,
      opponentId,
      outcome: null
    };
    
    if (!this.gameHistory.has(aiId)) {
      this.gameHistory.set(aiId, []);
    }
    
    this.gameHistory.get(aiId)!.push(historyEntry);
    
    // Keep only recent history
    const history = this.gameHistory.get(aiId)!;
    if (history.length > 1000) {
      history.splice(0, history.length - 1000);
    }
  }

  // Default configurations
  private getDefaultPersonality(): AIPersonality {
    return {
      aggression: 0.5,
      risk_tolerance: 0.5,
      pattern_recognition: 0.7,
      creativity: 0.3,
      consistency: 0.8,
      adaptation_speed: 0.4
    };
  }

  private initializeDefaultAI(): void {
    // Create default AI personalities
    this.createAIPlayer({
      name: 'Rookie AI',
      difficulty: 'easy',
      personality: {
        aggression: 0.3,
        risk_tolerance: 0.2,
        pattern_recognition: 0.4,
        creativity: 0.6,
        consistency: 0.5,
        adaptation_speed: 0.3
      }
    });
    
    this.createAIPlayer({
      name: 'Strategic AI',
      difficulty: 'hard',
      personality: {
        aggression: 0.4,
        risk_tolerance: 0.3,
        pattern_recognition: 0.9,
        creativity: 0.2,
        consistency: 0.9,
        adaptation_speed: 0.7
      }
    });
    
    this.createAIPlayer({
      name: 'Adaptive AI',
      difficulty: 'adaptive',
      personality: {
        aggression: 0.5,
        risk_tolerance: 0.5,
        pattern_recognition: 0.8,
        creativity: 0.4,
        consistency: 0.6,
        adaptation_speed: 0.9
      }
    });
  }

  // Public API methods
  updateDecisionOutcome(aiId: string, decision: AIDecision, outcome: 'win' | 'loss' | 'draw'): void {
    const history = this.gameHistory.get(aiId);
    if (!history) return;
    
    const recentDecisions = history.slice(-10);
    const decisionEntry = recentDecisions.find(entry => 
      entry.decision.action === decision.action &&
      Math.abs(entry.timestamp.getTime() - Date.now()) < 60000
    );
    
    if (decisionEntry) {
      decisionEntry.outcome = outcome;
      
      if (this.neuralNetwork && this.isLearningEnabled) {
        this.neuralNetwork.trainFromDecision(decisionEntry);
      }
    }
  }

  getAIPlayer(aiId: string): AIPlayerConfig | undefined {
    return this.aiPlayers.get(aiId);
  }

  getAvailableAIPlayers(): AIPlayerConfig[] {
    return Array.from(this.aiPlayers.values());
  }

  getPlayerProfile(playerId: string): PlayerProfile | undefined {
    return this.playerProfiles.get(playerId);
  }

  setLearningEnabled(enabled: boolean): void {
    this.isLearningEnabled = enabled;
  }

  exportLearningData(): any {
    return {
      aiPlayers: Array.from(this.aiPlayers.entries()),
      playerProfiles: Array.from(this.playerProfiles.entries()),
      gameHistory: Array.from(this.gameHistory.entries()),
      timestamp: new Date().toISOString()
    };
  }
}

// Supporting classes
class SimpleNeuralNetwork {
  private weights: number[][];
  private biases: number[];
  private learningRate = 0.01;

  constructor() {
    this.weights = this.initializeWeights();
    this.biases = this.initializeBiases();
  }

  async evaluate(gameState: any, action: any): Promise<number> {
    const input = this.stateToVector(gameState, action);
    return this.forward(input);
  }

  trainFromDecision(decisionEntry: GameHistoryEntry): void {
    if (!decisionEntry.outcome) return;
    
    const input = this.stateToVector(decisionEntry.gameState, decisionEntry.decision.action);
    const target = decisionEntry.outcome === 'win' ? 1 : (decisionEntry.outcome === 'draw' ? 0.5 : 0);
    
    this.backpropagate(input, target);
  }

  private stateToVector(gameState: any, action: any): number[] {
    return [Math.random(), Math.random(), Math.random(), Math.random()];
  }

  private forward(input: number[]): number {
    let sum = 0;
    for (let i = 0; i < input.length && i < this.weights[0].length; i++) {
      sum += input[i] * this.weights[0][i];
    }
    sum += this.biases[0];
    return 1 / (1 + Math.exp(-sum));
  }

  private backpropagate(input: number[], target: number): void {
    const output = this.forward(input);
    const error = target - output;
    const deltaOutput = error * output * (1 - output);
    
    for (let i = 0; i < input.length && i < this.weights[0].length; i++) {
      this.weights[0][i] += this.learningRate * deltaOutput * input[i];
    }
    
    this.biases[0] += this.learningRate * deltaOutput;
  }

  private initializeWeights(): number[][] {
    const weights = [];
    weights[0] = [];
    for (let i = 0; i < 4; i++) {
      weights[0][i] = (Math.random() - 0.5) * 0.1;
    }
    return weights;
  }

  private initializeBiases(): number[] {
    return [(Math.random() - 0.5) * 0.1];
  }
}

class PatternRecognizer {
  updatePatterns(profile: PlayerProfile, gameData: any): void {
    this.updateTimingPatterns(profile, gameData);
  }

  private updateTimingPatterns(profile: PlayerProfile, gameData: any): void {
    const timingPatternId = 'timing_consistency';
    const existingPattern = profile.gamePatterns.get(timingPatternId);
    
    if (existingPattern) {
      existingPattern.frequency++;
      existingPattern.confidence = Math.min(1, existingPattern.confidence + 0.1);
      existingPattern.lastSeen = new Date();
    } else {
      profile.gamePatterns.set(timingPatternId, {
        type: 'timing_pattern',
        pattern: [gameData.timeSpent],
        confidence: 0.3,
        frequency: 1,
        effectiveness: 0.5,
        lastSeen: new Date()
      });
    }
  }
}

class DifficultyAdapter {
  updateDifficulty(profile: PlayerProfile, gameData: any): void {
    const { result } = gameData;
    
    if (result === 'success') {
      profile.skillLevel = Math.min(100, profile.skillLevel + 1);
    } else if (result === 'failure') {
      profile.skillLevel = Math.max(0, profile.skillLevel - 0.5);
    }
  }

  calculateDifficulty(profile: PlayerProfile, gameType: string): {
    difficulty: string;
    reasoning: string;
    adjustments: Record<string, number>;
  } {
    const { skillLevel, emotionalState } = profile;
    
    let difficultyScore = skillLevel;
    
    if (emotionalState.frustration > 0.7) difficultyScore -= 10;
    if (emotionalState.confidence < 0.3) difficultyScore -= 5;
    if (emotionalState.engagement < 0.5) difficultyScore += 5;
    
    let targetDifficulty = 'medium';
    if (difficultyScore < 25) targetDifficulty = 'easy';
    else if (difficultyScore < 50) targetDifficulty = 'medium';
    else if (difficultyScore < 75) targetDifficulty = 'hard';
    else targetDifficulty = 'expert';
    
    return {
      difficulty: targetDifficulty,
      reasoning: `Skill: ${skillLevel}, Frustration: ${emotionalState.frustration.toFixed(2)}, Confidence: ${emotionalState.confidence.toFixed(2)}`,
      adjustments: {
        aiSpeed: difficultyScore / 100,
        aiAccuracy: difficultyScore / 100,
        hintFrequency: Math.max(0, 1 - difficultyScore / 100)
      }
    };
  }
}

interface GameHistoryEntry {
  timestamp: Date;
  aiId: string;
  gameState: any;
  decision: AIDecision;
  opponentId?: string;
  outcome: 'win' | 'loss' | 'draw' | null;
}

// Export singleton instance
export const advancedAIEngine = AdvancedAIEngine.getInstance();

/**
 * React Hook for AI integration
 */
export function useAI(gameType: string) {
  const [availableAI, setAvailableAI] = React.useState<AIPlayerConfig[]>([]);
  const [adaptiveDifficulty, setAdaptiveDifficulty] = React.useState<string>('medium');

  React.useEffect(() => {
    setAvailableAI(advancedAIEngine.getAvailableAIPlayers());
  }, []);

  const getAIDecision = React.useCallback(async (
    aiId: string,
    gameState: any,
    availableActions: any[],
    opponentId?: string
  ) => {
    return await advancedAIEngine.getAIDecision(aiId, gameState, availableActions, opponentId);
  }, []);

  const updatePlayerProfile = React.useCallback((playerId: string, gameData: any) => {
    advancedAIEngine.updatePlayerProfile(playerId, gameData);
    
    const difficulty = advancedAIEngine.getAdaptiveDifficulty(playerId, gameType);
    setAdaptiveDifficulty(difficulty.difficulty);
  }, [gameType]);

  const createAI = React.useCallback((config: Partial<AIPlayerConfig>) => {
    const aiId = advancedAIEngine.createAIPlayer(config);
    setAvailableAI(advancedAIEngine.getAvailableAIPlayers());
    return aiId;
  }, []);

  return {
    availableAI,
    adaptiveDifficulty,
    getAIDecision,
    updatePlayerProfile,
    createAI,
    updateDecisionOutcome: advancedAIEngine.updateDecisionOutcome.bind(advancedAIEngine),
    getPlayerProfile: advancedAIEngine.getPlayerProfile.bind(advancedAIEngine)
  };
}